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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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VI. Earth Life Emergence: Development of Body, Brain, Selves and Societies

7. Dynamic Ecosystems

Nielsen, S. N. Thermodynamics of an Ecosystem Interpreted as a Hierarchy of Embedded Systems. Ecological Modelling. 135/2-3, 2000. An integration of nonequilibrium thermodynamics and network perspectives.

Niwa, Hiro-Sato. Power-law Scaling in Dimension-to-Biomass Relationship of Fish Schools. Journal of Theoretical Biology. 235/3, 2005. As another microcosm which exemplifies nature’s mathematical universality.

Motivated by the finding that there is some biological universality in the relationship between school geometry and school biomass of various pelagic fishes in various conditions, I here establish a scaling law for school dimensions: the school diameter increases as a power-law function of school biomass. (419)

Norberg, Jon. Biodiversity and Ecosystem Functioning: A Complex Adaptive Systems Approach. Limnology and Oceanography. 49/4, Part 2, 2004. From a supplement issue on Planktonic Biodiversity: Scaling Up and Down, a case that CAS theory can explain how dynamic spatial hierarchies form from individual entities which locally interact. Also in the same issue: Leibold, Mathew and Jon Norberg. Biodiversity in Metacommunities: Plankton as Complex Adaptive systems?

Nordbotten, Jan, et al. Ecological and Evolutionary Dynamics of Interconnectedness and Modularity. Proceedings of the National Academy of Sciences. 115/750, 2018. This paper by senior scientists Nordbotten (University of Bergen, Norway) Simon Levin, Eors Szathmary and Nils Stenseth, reviewed by David Krakauer and Gunter Wagner, achieves a latest affirmation of animal interactivities within modular groupings as a pervasive, structural way that living systems evolve, sustain and prevail.

We develop a theoretical framework for linking microprocesses (i.e., population dynamics and evolution through natural selection) with macrophenomena (such as interconnectedness and modularity within an ecological system). This is achieved by developing a measure of interconnectedness for population distributions defined on a trait space, in combination with an evolution equation for the population distribution. With this contribution, we provide a platform for understanding under what environmental, ecological, and evolutionary conditions ecosystems evolve toward being more or less modular. Thus we are able to decompose the overall driver of changes at the macro level (such as interconnectedness) into three components: (i) ecologically driven change, (ii) evolutionarily driven change, and (iii) environmentally driven change. (Abstract)

O’Dwyer, James. The Hidden Laws of Ecosystems. Nautilus. Online October, 2015. While a tacit mindset in this endeavor is seen to set aside or deny universal principles and patterns such as physics admits, a University of Illinois theoretical ecologist contends that attention to their actual presence can advance the field. An increasing number of studies, lately aided by genetic info, are indeed finding a reliable persistence, such as UC Berkeley’s John Harte (search) about species and spatial areas. O’Dywer is a contributor along with 15 coauthors including Jennifer Dunne, John Harte, and Geoffrey West to a manifesto On Theory in Ecology in Bioscience (Pablo Marquet, et al, July 2014)

Power laws are common in science, and are the defining feature of universality in physics. They describe the strength of magnets as temperature increases, earthquake frequency versus size, and city productivity as a function of population. For many ecologists, the species-area curve strikes a nerve. It suggests that at a large enough scale, the specific detail of an ecosystem—the “entangled bank” that lies so near and dear to the ecologist’s heart—simply doesn’t matter. The idiosyncrasies wash out, and ecological systems start to look surprisingly similar to a broad swathe of disparate systems in other sciences.

Olesen, Jens, et al. The Modularity of Pollination Networks. Proceedings of the National Academy of Sciences. 104/19891, 2007. An analysis of a large database of some 10,000 cases of mutualistic plant-animal interactions demonstrates a constant formation of modular components. An understanding of critical nodes in such webs can then aid efforts to preserve biodiversity.

The omnipresence of modularity and other structural properties, e.g., nestedness, in large pollination networks may change our view on the structuring of biodiversity. Our study shows that modules are small blocks of species, candidating as manageable study objects, and that their study may bridge evolutionary and functional ecology. (19894)

Pahl-Wostl, Claudia. The Dynamic Nature of Ecosystems. Chichester, UK: Wiley, 1995. In response to the old fragmented, Newtonian mechanistic paradigm, a holistic, relational approach is proposed that can engage the pervasive “pattern of interactions” in self-organized environments.

Pascual, Mercedes and Frederic Guichard. Criticality and Disturbance in Spatial Ecological Systems. Trends in Ecology and Evolution. 20/2, 2005. Three modes are identified: phase transitions, self-organized criticality and ‘robust’ criticality; with regard to spatial patterns, temporal dynamics and threshold behavior.

Criticality has been an appealing ecological concept from two different perspectives: first, as an explanation for scale-invariant patterns in nature, and second, as a mechanism underlying drastic change, in the form of either large unpredictable temporal fluctuations (SOC) or sudden state shifts by small perturbations (classical phase transitions). (94)

Pascual, Mercedes and Jennifer Dunne, eds. Ecological Networks. Oxford: Oxford University Press, 2006. A collection from the Santa Fe Institute that marks a new level and phase of mathematical quantification and experimental verification. Darwin’s “tangled bank” is at last discernible in both structure and dynamics via the many varieties of complex systems theory.

Peacor, Scott, et al. Phenotypic Plasticity and Species Coexistence: Modeling Food Webs as Complex Adaptive Systems. Pascual, Mercedes and Jennifer Dunne, eds. Ecological Networks. Oxford: Oxford University Press, 2006. The chapters in this book, from the certain experience of their authors, each convey a somewhat different terminology and emphasis. Some talk of network nodes and links. In this paper, following Holland, Gell-Mann, Morowitz, Bar-Yam, and especially Levin, ecosystems are said to involve diverse individuals or agents whose local interactions gives rise to an emergent, hierarchical scale.

Following a CAS approach, we present a computational tool in which the dynamics and structure of a model community emerge from interacting individuals that adapt to their environment. (247)

Peters, Debra, et al. An Integrated View of Complex Landscapes: A Big Data-Model Integration Approach to Transdisciplinary Science. BioScience. 68/9, 2018. A significant proposal by twenty-two working environmentalists across the USA such as the Dept. of Agriculture, UCLA, ASU, and other flora and fauna agencies and colleges, that the time has come, as Ashley Shade et al below agree, to strive for a whole Earth systemic synthesis across every micro to macro natural realm.

The Earth is a complex system comprising many interacting spatial and temporal scales. We developed a transdisciplinary data-model integration (TDMI) approach to understand, predict, and manage for these complex dynamics that focuses on spatiotemporal modeling and cross-scale interactions. Our approach employs human-centered machine-learning strategies supported by a data science integration system (DSIS). Applied to ecological problems, our approach integrates knowledge and data on (a) biological processes, (b) spatial heterogeneity in the land surface template, and (c) variability in environmental drivers using data and knowledge drawn from multiple lines of evidence (i.e., observations, experimental manipulations, analytical and numerical models, products from imagery, conceptual model reasoning, and theory). (Abstract)

Pigolotti, Simone, et al. Stochastic Spatial Models in Ecology. Journal of Statistical Physics. 172/1, 2018. SP, Okinawa Institute of Science, Massimo Cencini and Consiglio Nazionale delle Ricerch, Rome, Daniel Molina, Basque Center for Applied Mathematics, and Miguel Munoz, University of Granada, Spain provide a good example of later 2010s (re)unifications across this widest span from lively physical substrates to active flora and fauna environments.

Ecosystems display a complex spatial organization. Ecologists have long tried to characterize them by looking at how different measures of biodiversity change across spatial scales. Ecological neutral theory has provided simple predictions accounting for general empirical patterns in communities of competing species. In this review, we emphasize the connection between spatial ecological models and the physics of non-equilibrium phase transitions and how concepts developed in statistical physics translate in population dynamics, and vice versa. We conclude by discussing models incorporating non-neutral effects in the form of spatial and temporal disorder, and analyze how their predictions deviate from those of purely neutral theories. (Abstract excerpt)

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